iOptimizerMLEM.cc

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/*
  Implementation of class iOptimizerMLEM

  - separators: X
  - doxygen: X
  - default initialization: X
  - CASTOR_DEBUG: 
  - CASTOR_VERBOSE: 
*/

#include "iOptimizerMLEM.hh"
#include "sOutputManager.hh"

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iOptimizerMLEM::iOptimizerMLEM() : vOptimizer()
{
  // ---------------------------
  // Mandatory member parameters
  // ---------------------------

  // Initial value at 1
  m_initialValue = 1.;
  // Only one backward image for MLEM
  m_nbBackwardImages = 1;
  // MLEM does not accept penalties
  //m_penaltyEnergyFunctionDerivativesOrder = 0;
  // MLEM is compatible with listmode and histogram data
  m_listmodeCompatibility = true;
  m_histogramCompatibility = true;

  // --------------------------
  // Specific member parameters
  // --------------------------

  m_dataSpaceDenominatorThreshold = -1.;
  m_minimumImageUpdateFactor = -1.;
  m_maximumImageUpdateFactor = -1.;
}

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iOptimizerMLEM::~iOptimizerMLEM()
{
}

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void iOptimizerMLEM::ShowHelpSpecific()
{
  cout << "This optimizer is the standard MLEM (for Maximum Likelihood Expectation Maximization)." << endl;
  cout << "If subsets are used, it naturally becomes the OSEM optimizer." << endl;
  cout << "The following options can be used (in this particular order when provided as a list):" << endl;
  cout << "  initial image value: to set the uniform voxel value for the initial image" << endl;
  cout << "  denominator threshold: to set the threshold of the data space denominator under which the ratio is set to 1" << endl;
  cout << "  minimum image update: to set the minimum of the image update factor under which it stays constant (0 or a negative value" << endl;
  cout << "                        means no minimum thus allowing a 0 update)" << endl;
  cout << "  maximum image update: to set the maximum of the image update factor over which it stays constant (0 or a negative value means" << endl;
  cout << "                        no maximum)" << endl;
}

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int iOptimizerMLEM::ReadConfigurationFile(const string& a_configurationFile)
{
  string key_word = "";
  // Read the initial image value option
  key_word = "initial image value";
  if (ReadDataASCIIFile(a_configurationFile, key_word, &m_initialValue, 1, KEYWORD_MANDATORY))
  {
    Cerr("***** iOptimizerMLEM::ReadAndCheckConfigurationFile() -> Failed to get the '" << key_word << "' keyword !" << endl);
    return 1;
  }
  // Read the denominator threshold option
  key_word = "denominator threshold";
  if (ReadDataASCIIFile(a_configurationFile, key_word, &m_dataSpaceDenominatorThreshold, 1, KEYWORD_MANDATORY))
  {
    Cerr("***** iOptimizerMLEM::ReadAndCheckConfigurationFile() -> Failed to get the '" << key_word << "' keyword !" << endl);
    return 1;
  }
  // Read the minimum image update option
  key_word = "minimum image update";
  if (ReadDataASCIIFile(a_configurationFile, key_word, &m_minimumImageUpdateFactor, 1, KEYWORD_MANDATORY))
  {
    Cerr("***** iOptimizerMLEM::ReadAndCheckConfigurationFile() -> Failed to get the '" << key_word << "' keyword !" << endl);
    return 1;
  }
  // Read the maximum image update option
  key_word = "maximum image update";
  if (ReadDataASCIIFile(a_configurationFile, key_word, &m_maximumImageUpdateFactor, 1, KEYWORD_MANDATORY))
  {
    Cerr("***** iOptimizerMLEM::ReadAndCheckConfigurationFile() -> Failed to get the '" << key_word << "' keyword !" << endl);
    return 1;
  }
  // Normal end
  return 0;
}

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int iOptimizerMLEM::ReadOptionsList(const string& a_optionsList)
{
  // There are 4 floating point variables as options
  FLTNB options[4];
  
  // Read them
  if (ReadStringOption(a_optionsList, options, 4, ",", "MLEM configuration"))
  {
    Cerr("***** iOptimizerMLEM::ReadAndCheckConfigurationFile() -> Failed to correctly read the list of options !" << endl);
    return 1;
  }
  // Affect options
  m_initialValue = options[0];
  m_dataSpaceDenominatorThreshold = options[1];
  m_minimumImageUpdateFactor = options[2];
  m_maximumImageUpdateFactor = options[3];
  
  // Normal end
  return 0;
}

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int iOptimizerMLEM::CheckSpecificParameters()
{
  // Check that initial image value is strictly positive
  if (m_initialValue<=0.)
  {
    Cerr("***** iOptimizerMLEM->Initialize() -> Provided initial image value (" << m_initialValue << ") must be strictly positive !" << endl);
    return 1;
  }
  // Check that denominator threshold value is strictly positive
  if (m_dataSpaceDenominatorThreshold<=0.)
  {
    Cerr("***** iOptimizerMLEM->Initialize() -> Provided data space denominator threshold (" << m_dataSpaceDenominatorThreshold << ") must be strictly positive !" << endl);
    return 1;
  }
  // Check that maximum image update factor is higher than the minimum
  if (m_minimumImageUpdateFactor>0. && m_maximumImageUpdateFactor>0. && m_maximumImageUpdateFactor<m_minimumImageUpdateFactor)
  {
    Cerr("***** iOptimizerMLEM->Initialize() -> Provided minimum/maximum (" << m_minimumImageUpdateFactor << "/" << m_maximumImageUpdateFactor << " are inconsistent !" << endl);
    return 1;
  }
  // Normal end
  return 0;
}

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int iOptimizerMLEM::InitializeSpecific()
{
  // Verbose
  if (m_verbose>=2)
  {
    Cout("iOptimizerMLEM::Initialize() -> Use the MLEM optimizer" << endl);
    if (m_verbose>=3)
    {
      Cout("  --> Initial image value: " << m_initialValue << endl);
      Cout("  --> Data space denominator threshold: " << m_dataSpaceDenominatorThreshold << endl);
      if (m_minimumImageUpdateFactor>0.) Cout("  --> Minimum image update factor: " << m_minimumImageUpdateFactor << endl);
      else Cerr("!!!!! The minimum update value is not set, if using subsets, voxels could be trapped in 0 value causing some negative bias !" << endl);
      if (m_maximumImageUpdateFactor>0.) Cout("  --> Maximum image update factor: " << m_maximumImageUpdateFactor << endl);
    }
  }
  // Normal end
  return 0;
}

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int iOptimizerMLEM::SensitivitySpecificOperations( FLTNB a_data, FLTNB a_forwardModel, FLTNB* ap_weight,
                                                   FLTNB a_multiplicativeCorrections, FLTNB a_additiveCorrections,
                                                   FLTNB a_quantificationFactor, oProjectionLine* ap_Line )
{
  // Line weight here is simply 1
  *ap_weight = 1.;
  // That's all
  return 0;
}

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int iOptimizerMLEM::DataSpaceSpecificOperations( FLTNB a_data, FLTNB a_forwardModel, FLTNB* ap_backwardValues,
                                                 FLTNB a_multiplicativeCorrections, FLTNB a_additiveCorrections,
                                                 FLTNB a_quantificationFactor, oProjectionLine* ap_Line )
{
  // Truncate data to 0 if negative
  if (a_data<0.) a_data = 0.;
  // If the foward model is to close to zero, then ignore this data (set to 1 and not 0 because this line is taken into account in the sensitivity)
  if (a_forwardModel>m_dataSpaceDenominatorThreshold) *ap_backwardValues = a_data / a_forwardModel;
  else *ap_backwardValues = 1.;
  // That's all
  return 0;
}

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int iOptimizerMLEM::ImageSpaceSpecificOperations( FLTNB a_currentImageValue, FLTNB* ap_newImageValue,
                                                  FLTNB a_sensitivity, FLTNB* ap_correctionValues )
{
  // Compute image update factor
  FLTNB image_update_factor = *ap_correctionValues / a_sensitivity;
  // Apply minimum image update factor
  if ( m_minimumImageUpdateFactor > 0. && image_update_factor < m_minimumImageUpdateFactor ) image_update_factor = m_minimumImageUpdateFactor;
  // Apply maximum image update factor
  if ( m_maximumImageUpdateFactor > 0. && image_update_factor > m_maximumImageUpdateFactor ) image_update_factor = m_maximumImageUpdateFactor;
  // Update image
  *ap_newImageValue = a_currentImageValue * image_update_factor;
  // End
  return 0;
}

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